Moon, K. W., Jung, S. H., … & Kim, D. (2024) Cardiovascular risk according to genetic predisposition to gout, lifestyle, and metabolic health across prospective European and Korean cohorts.
The Gout PRS was generated based on the large-scale gout meta GWAS summary statistics (6,544 cases and 437,989 controls) from the Global Urate Genetics Consortium(1) and FinnGen Consortium (Data Freeze R9)(2) using the Bayesian polygenic prediction method PRS-CS.(3) Individual PRSs were computed from beta coefficients as the weighted sum of the risk alleles by applying PLINK version 1.90 with the --score command.(4)
- Köttgen A, Albrecht E, Teumer A, Vitart V, Krumsiek J, Hundertmark C, et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nature genetics. 2013;45(2):145-54.
- Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508-18.
- Ge T, Chen C-Y, Ni Y, Feng Y-CA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature communications. 2019;10(1):1776.
- Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4(1):s13742-015-0047-8.